Abstract: A dynamic counterbalance system is provided. The dynamic counterbalance system can include a compound pulley system and an adjustment arm. The compound pulley system can include a frame, a spring, a variable radius pulley, a plurality of mounted pulleys, a plurality of moveable pulleys, and a plurality of lines. The adjustment arm can include a mountable support, a spool support, a spool, an arm inner support, a rod support, an extended rod, a rod end cap, a traveling pulley, a dynamic pulley, and a plurality of lines.

Abstract: In some embodiments, there is provided a system, which comprises at least one computer that is communicatively coupled with at least one sensor. The at least one sensor is embedded in a brain and configured to detect a plurality of activity data associated with neurons of the brain, implement a compression algorithm on the plurality of activity data by comparing a first entropy value associated with a first set of data points of the plurality of activity data with a second entropy value associated with a second set of data points of the plurality of activity data, and combining the first set of data points responsive to determining that the first entropy value is lower than the second entropy value, and transmit the combination of the first set of data points to the at least one computer.

Abstract: In some embodiments, there is provided a system, which comprises at least one computer that is communicatively coupled with at least one sensor. The at least one sensor is embedded in a brain and configured to detect a plurality of activity data associated with neurons of the brain, implement a compression algorithm on the plurality of activity data by comparing a first entropy value associated with a first set of data points of the plurality of activity data with a second entropy value associated with a second set of data points of the plurality of activity data, and combining the first set of data points responsive to determining that the first entropy value is lower than the second entropy value, and transmit the combination of the first set of data points to the at least one computer.

Abstract: Systems and methods that use computer vision techniques in connection with robotic surgery are discussed. A robotic surgery system may include an implantable device engagement sub-system, a targeting sub-system, and/or an insertion verification sub-system. The system may use computer vision techniques to facilitate implanting a micro-manufactured bio-compatible electrode device in biological tissue (e.g., neurological tissue such as the brain) using robotic assemblies. The system can attach, via robotic manipulation, the electrode to an engagement element of an insertion needle.

Abstract: Systems and methods that use computer vision techniques in connection with robotic surgery are discussed. A robotic surgery system may include an implantable device engagement sub-system, a targeting sub-system, and/or an insertion verification sub-system. The system may use computer vision techniques to facilitate implanting a micro-manufactured bio-compatible electrode device in biological tissue (e.g., neurological tissue such as the brain) using robotic assemblies. The system can attach, via robotic manipulation, the electrode to an engagement element of an insertion needle.

Abstract: Artificial control of a prosthetic device is provided. A brain machine interface contains a mapping of neural signals and corresponding intention estimating kinematics (e.g. positions and velocities) of a limb trajectory. The prosthetic device is controlled by the brain machine interface. During the control of the prosthetic device, a modified brain machine interface is developed by modifying the vectors of the velocities defined in the brain machine interface. The modified brain machine interface includes a new mapping of the neural signals and the intention estimating kinematics that can now be used to control the prosthetic device using recorded neural brain signals from a user of the prosthetic device. In one example, the intention estimating kinematics of the original and modified brain machine interface includes a Kalman filter modeling velocities as intentions and positions as feedback.

Abstract: Artificial control of a prosthetic device is provided. A brain machine interface contains a mapping of neural signals and corresponding intention estimating kinematics (e.g. positions and velocities) of a limb trajectory. The prosthetic device is controlled by the brain machine interface. During the control of the prosthetic device, a modified brain machine interface is developed by modifying the vectors of the velocities defined in the brain machine interface. The modified brain machine interface includes a new mapping of the neural signals and the intention estimating kinematics that can now be used to control the prosthetic device using recorded neural brain signals from a user of the prosthetic device. In one example, the intention estimating kinematics of the original and modified brain machine interface includes a Kalman filter modeling velocities as intentions and positions as feedback.